Documentation and Support

This page consists of documentation to get started, and expected data layout for various algorithms. At the moment,
it is embarassingly small.

Installation

Please look at the INSTALL file in the top level directory.

Dataset Layout

MaTEx supports sparse data format. The dataset is expected to have one sample/vector on each line, separated by , or :.

Sample Data Layout for Classification Algorithms

Dataset(s) for Classification algorithms are expected to adhere to libsvm sparse data format. An example is here, where each line in the
dataset is expected to look like as follows:

class col1:val1 col2:val2 col3:val3

Following format is also acceptable (equivalent to CSV format):

class,col1,val1,col2,val2

MaTEx ignores empty lines. The dataset may contain any arbitrary number of spaces.

Sample Data Layout for Clustering Algorithms

Dataset(s) for Clustering algorithms are expected to follow a sparse data format as follows. Since datasets for clustering do not have
class variable, each line in the dataset is expected to look like as follows:

col1:val1 col2:val2 col3:val3

Following format is also acceptable (equivalent to CSV format):

col1,val1,col2,val2

The dataset may contain any arbitrary number of spaces.

Sample Data Layout for Association Rule Mining (ARM) Algorithms

Dataset(s) for ARM are expected to follow a sparse data format as follows. The datasets for ARM are not expected to have val associated with
column. Hence, each line in the dataset is expected to look like:

col1 col2 col3 col4

Following format is also acceptable (equivalent to CSV format):

col1,col2,col3,col4

The dataset may contain any arbitrary number of spaces.

Running MaTEx algorithms

Each algorithm requires different parameters:

SVM Example

SVM requires a training set and a testing set in the libsvm format (see above). The hyperparameters need to be provided as well (C and sigmasqr).
As an example to run svm with 16 processes on adult training set (a9a) and testing set (a9a.t) with parameters C and sigmasqr, 32 and 64, respectively: